Overview

Dataset statistics

Number of variables20
Number of observations586672
Missing cells71
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.5 MiB
Average record size in memory160.0 B

Variable types

Categorical8
Numeric12

Alerts

id has a high cardinality: 586672 distinct values High cardinality
name has a high cardinality: 446474 distinct values High cardinality
artists has a high cardinality: 114030 distinct values High cardinality
id_artists has a high cardinality: 115062 distinct values High cardinality
release_date has a high cardinality: 19700 distinct values High cardinality
danceability is highly correlated with valence and 2 other fieldsHigh correlation
energy is highly correlated with loudness and 1 other fieldsHigh correlation
loudness is highly correlated with energy and 1 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
valence is highly correlated with danceabilityHigh correlation
tempo is highly correlated with danceability and 1 other fieldsHigh correlation
time_signature is highly correlated with danceability and 1 other fieldsHigh correlation
id is uniformly distributed Uniform
id has unique values Unique
popularity has 44690 (7.6%) zeros Zeros
key has 74950 (12.8%) zeros Zeros
instrumentalness has 205083 (35.0%) zeros Zeros

Reproduction

Analysis started2022-09-14 20:13:39.840114
Analysis finished2022-09-14 20:15:20.557563
Duration1 minute and 40.72 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct586672
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
35iwgR4jXetI318WEWsa1Q
 
1
6cHlho8Qe04uAIa1hd6efJ
 
1
1AL2EDY1U2dLL0WqQGtNu0
 
1
4vsj6KApKrZnQnF76Zve2u
 
1
5D0srsR8tggP6mLAdBn8d9
 
1
Other values (586667)
586667 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12906784
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique586672 ?
Unique (%)100.0%

Sample

1st row35iwgR4jXetI318WEWsa1Q
2nd row021ht4sdgPcrDgSk7JTbKY
3rd row07A5yehtSnoedViJAZkNnc
4th row08FmqUhxtyLTn6pAh6bk45
5th row08y9GfoqCWfOGsKdwojr5e

Common Values

ValueCountFrequency (%)
35iwgR4jXetI318WEWsa1Q1
 
< 0.1%
6cHlho8Qe04uAIa1hd6efJ1
 
< 0.1%
1AL2EDY1U2dLL0WqQGtNu01
 
< 0.1%
4vsj6KApKrZnQnF76Zve2u1
 
< 0.1%
5D0srsR8tggP6mLAdBn8d91
 
< 0.1%
6GbE5GD4xCcnJvpjsasjiB1
 
< 0.1%
3RFI8uU3RQt4QJoluTYPdm1
 
< 0.1%
1Bw4w65vm06L97nwvj0JdO1
 
< 0.1%
5CjJtWtT5CIE4QhgHAdhSm1
 
< 0.1%
7LXQvp92lpBY2w878B1b0v1
 
< 0.1%
Other values (586662)586662
> 99.9%

Length

2022-09-15T01:45:20.674576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
35iwgr4jxeti318wewsa1q1
 
< 0.1%
0brxjhrngq3w4v9frnsfhu1
 
< 0.1%
0grxu6gkvncvmjbsea0uhe1
 
< 0.1%
2u7t2vcrlxkp69um0mdes21
 
< 0.1%
0igi1ucz84pyevetnl1lgp1
 
< 0.1%
07a5yehtsnoedvijazknnc1
 
< 0.1%
08fmquhxtyltn6pah6bk451
 
< 0.1%
08y9gfoqcwfogskdwojr5e1
 
< 0.1%
0dd9imxtatgwsmsad69kzt1
 
< 0.1%
1klkkacg16o5crqpiaf1tz1
 
< 0.1%
Other values (586662)586662
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0278639
 
2.2%
1275299
 
2.1%
2274969
 
2.1%
4274047
 
2.1%
3273494
 
2.1%
5272518
 
2.1%
6271098
 
2.1%
7256601
 
2.0%
s200017
 
1.5%
y199765
 
1.5%
Other values (52)10330337
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5174821
40.1%
Uppercase Letter5157371
40.0%
Decimal Number2574592
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s200017
 
3.9%
y199765
 
3.9%
e199651
 
3.9%
i199593
 
3.9%
t199436
 
3.9%
w199423
 
3.9%
r199363
 
3.9%
v199352
 
3.9%
k199288
 
3.9%
h199229
 
3.8%
Other values (16)3179704
61.4%
Uppercase Letter
ValueCountFrequency (%)
A199714
 
3.9%
C199535
 
3.9%
M199508
 
3.9%
F199310
 
3.9%
B199193
 
3.9%
J199101
 
3.9%
H199084
 
3.9%
L198897
 
3.9%
K198842
 
3.9%
D198680
 
3.9%
Other values (16)3165507
61.4%
Decimal Number
ValueCountFrequency (%)
0278639
10.8%
1275299
10.7%
2274969
10.7%
4274047
10.6%
3273494
10.6%
5272518
10.6%
6271098
10.5%
7256601
10.0%
9199538
7.8%
8198389
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin10332192
80.1%
Common2574592
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s200017
 
1.9%
y199765
 
1.9%
A199714
 
1.9%
e199651
 
1.9%
i199593
 
1.9%
C199535
 
1.9%
M199508
 
1.9%
t199436
 
1.9%
w199423
 
1.9%
r199363
 
1.9%
Other values (42)8336187
80.7%
Common
ValueCountFrequency (%)
0278639
10.8%
1275299
10.7%
2274969
10.7%
4274047
10.6%
3273494
10.6%
5272518
10.6%
6271098
10.5%
7256601
10.0%
9199538
7.8%
8198389
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII12906784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0278639
 
2.2%
1275299
 
2.1%
2274969
 
2.1%
4274047
 
2.1%
3273494
 
2.1%
5272518
 
2.1%
6271098
 
2.1%
7256601
 
2.0%
s200017
 
1.5%
y199765
 
1.5%
Other values (52)10330337
80.0%

name
Categorical

HIGH CARDINALITY

Distinct446474
Distinct (%)76.1%
Missing71
Missing (%)< 0.1%
Memory size4.5 MiB
Summertime
 
101
Intro
 
92
Year 3000
 
91
Hold On
 
87
2000 Years
 
76
Other values (446469)
586154 

Length

Max length529
Median length242
Mean length20.24399379
Min length1

Characters and Unicode

Total characters11875147
Distinct characters4678
Distinct categories22 ?
Distinct scripts17 ?
Distinct blocks34 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique376093 ?
Unique (%)64.1%

Sample

1st rowCarve
2nd rowCapítulo 2.16 - Banquero Anarquista
3rd rowVivo para Quererte - Remasterizado
4th rowEl Prisionero - Remasterizado
5th rowLady of the Evening

Common Values

ValueCountFrequency (%)
Summertime101
 
< 0.1%
Intro92
 
< 0.1%
Year 300091
 
< 0.1%
Hold On87
 
< 0.1%
2000 Years76
 
< 0.1%
Home74
 
< 0.1%
Baby72
 
< 0.1%
Angel68
 
< 0.1%
Stay68
 
< 0.1%
Forever65
 
< 0.1%
Other values (446464)585807
99.9%
(Missing)71
 
< 0.1%

Length

2022-09-15T01:45:20.824491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
118721
 
5.3%
the39475
 
1.8%
in21286
 
1.0%
a21150
 
1.0%
i18791
 
0.8%
de17376
 
0.8%
you17318
 
0.8%
me15448
 
0.7%
of14574
 
0.7%
no14157
 
0.6%
Other values (236150)1923628
86.6%

Most occurring characters

ValueCountFrequency (%)
1635323
 
13.8%
e985846
 
8.3%
a831482
 
7.0%
o622983
 
5.2%
i610106
 
5.1%
n553982
 
4.7%
r519226
 
4.4%
t445938
 
3.8%
l378543
 
3.2%
s361331
 
3.0%
Other values (4668)4930387
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7471048
62.9%
Uppercase Letter1731546
 
14.6%
Space Separator1635323
 
13.8%
Other Letter297495
 
2.5%
Decimal Number281270
 
2.4%
Other Punctuation222317
 
1.9%
Dash Punctuation117593
 
1.0%
Close Punctuation47899
 
0.4%
Open Punctuation47843
 
0.4%
Nonspacing Mark16872
 
0.1%
Other values (12)5941
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
י10097
 
3.4%
ו7873
 
2.6%
ה6906
 
2.3%
ל6228
 
2.1%
א4922
 
1.7%
ר4652
 
1.6%
4594
 
1.5%
4527
 
1.5%
ב4203
 
1.4%
4167
 
1.4%
Other values (4110)239326
80.4%
Lowercase Letter
ValueCountFrequency (%)
e985846
13.2%
a831482
11.1%
o622983
 
8.3%
i610106
 
8.2%
n553982
 
7.4%
r519226
 
6.9%
t445938
 
6.0%
l378543
 
5.1%
s361331
 
4.8%
u276252
 
3.7%
Other values (190)1885359
25.2%
Uppercase Letter
ValueCountFrequency (%)
S135434
 
7.8%
M134651
 
7.8%
T132102
 
7.6%
A112973
 
6.5%
L96399
 
5.6%
D90687
 
5.2%
B86180
 
5.0%
C85044
 
4.9%
R82416
 
4.8%
I80670
 
4.7%
Other values (152)694990
40.1%
Nonspacing Mark
ValueCountFrequency (%)
3609
21.4%
3300
19.6%
2910
17.2%
1811
10.7%
1296
 
7.7%
832
 
4.9%
686
 
4.1%
620
 
3.7%
526
 
3.1%
360
 
2.1%
Other values (27)922
 
5.5%
Other Punctuation
ValueCountFrequency (%)
.57370
25.8%
,47649
21.4%
'43395
19.5%
:24603
11.1%
"17199
 
7.7%
/12174
 
5.5%
&5684
 
2.6%
!5147
 
2.3%
?4310
 
1.9%
;1479
 
0.7%
Other values (21)3307
 
1.5%
Other Symbol
ValueCountFrequency (%)
°53
31.0%
33
19.3%
24
14.0%
12
 
7.0%
®12
 
7.0%
7
 
4.1%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
Other values (13)18
 
10.5%
Math Symbol
ValueCountFrequency (%)
~418
41.6%
+273
27.2%
|112
 
11.2%
=63
 
6.3%
>52
 
5.2%
<44
 
4.4%
×12
 
1.2%
10
 
1.0%
5
 
0.5%
5
 
0.5%
Other values (9)10
 
1.0%
Decimal Number
ValueCountFrequency (%)
063921
22.7%
153122
18.9%
250631
18.0%
920960
 
7.5%
319856
 
7.1%
416338
 
5.8%
515855
 
5.6%
813580
 
4.8%
613561
 
4.8%
713441
 
4.8%
Other values (3)5
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)45124
94.2%
]2375
 
5.0%
196
 
0.4%
164
 
0.3%
17
 
< 0.1%
12
 
< 0.1%
}5
 
< 0.1%
4
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(45056
94.2%
[2379
 
5.0%
196
 
0.4%
164
 
0.3%
17
 
< 0.1%
12
 
< 0.1%
11
 
< 0.1%
4
 
< 0.1%
{3
 
< 0.1%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
2825
95.1%
81
 
2.7%
55
 
1.9%
4
 
0.1%
ˈ2
 
0.1%
ʻ1
 
< 0.1%
ˇ1
 
< 0.1%
ˋ1
 
< 0.1%
ـ1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-117055
99.5%
375
 
0.3%
115
 
0.1%
18
 
< 0.1%
16
 
< 0.1%
14
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´168
67.2%
`77
30.8%
^2
 
0.8%
¨1
 
0.4%
˙1
 
0.4%
΄1
 
0.4%
Private Use
ValueCountFrequency (%)
4
40.0%
2
20.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
Currency Symbol
ValueCountFrequency (%)
$179
92.7%
7
 
3.6%
£3
 
1.6%
¥3
 
1.6%
¢1
 
0.5%
Letter Number
ValueCountFrequency (%)
11
52.4%
5
23.8%
3
 
14.3%
1
 
4.8%
1
 
4.8%
Control
ValueCountFrequency (%)
„1
20.0%
“1
20.0%
‚1
20.0%
’1
20.0%
†1
20.0%
Final Punctuation
ValueCountFrequency (%)
693
74.5%
183
 
19.7%
»54
 
5.8%
Initial Punctuation
ValueCountFrequency (%)
162
65.3%
«54
 
21.8%
32
 
12.9%
Format
ValueCountFrequency (%)
7
63.6%
3
27.3%
1
 
9.1%
Space Separator
ValueCountFrequency (%)
1635323
100.0%
Connector Punctuation
ValueCountFrequency (%)
_127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9097167
76.6%
Common2357988
 
19.9%
Han103052
 
0.9%
Cyrillic98175
 
0.8%
Hebrew80512
 
0.7%
Thai77395
 
0.7%
Katakana24826
 
0.2%
Hiragana24662
 
0.2%
Greek7349
 
0.1%
Arabic2217
 
< 0.1%
Other values (7)1804
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
2841
 
2.8%
2115
 
2.1%
1986
 
1.9%
1870
 
1.8%
1421
 
1.4%
1366
 
1.3%
1176
 
1.1%
1150
 
1.1%
949
 
0.9%
939
 
0.9%
Other values (3432)87239
84.7%
Hangul
ValueCountFrequency (%)
32
 
2.6%
26
 
2.2%
26
 
2.2%
23
 
1.9%
22
 
1.8%
22
 
1.8%
20
 
1.7%
19
 
1.6%
19
 
1.6%
19
 
1.6%
Other values (324)981
81.1%
Latin
ValueCountFrequency (%)
e985846
 
10.8%
a831482
 
9.1%
o622983
 
6.8%
i610106
 
6.7%
n553982
 
6.1%
r519226
 
5.7%
t445938
 
4.9%
l378543
 
4.2%
s361331
 
4.0%
u276252
 
3.0%
Other values (213)3511478
38.6%
Common
ValueCountFrequency (%)
1635323
69.4%
-117055
 
5.0%
063921
 
2.7%
.57370
 
2.4%
153122
 
2.3%
250631
 
2.1%
,47649
 
2.0%
)45124
 
1.9%
(45056
 
1.9%
'43395
 
1.8%
Other values (127)199342
 
8.5%
Katakana
ValueCountFrequency (%)
2066
 
8.3%
1257
 
5.1%
1084
 
4.4%
1056
 
4.3%
977
 
3.9%
912
 
3.7%
778
 
3.1%
675
 
2.7%
597
 
2.4%
572
 
2.3%
Other values (74)14852
59.8%
Hiragana
ValueCountFrequency (%)
3311
 
13.4%
1715
 
7.0%
1197
 
4.9%
948
 
3.8%
895
 
3.6%
746
 
3.0%
746
 
3.0%
740
 
3.0%
656
 
2.7%
626
 
2.5%
Other values (70)13082
53.0%
Cyrillic
ValueCountFrequency (%)
а9712
 
9.9%
о8175
 
8.3%
е7528
 
7.7%
и5574
 
5.7%
н5527
 
5.6%
т4935
 
5.0%
р4330
 
4.4%
л4227
 
4.3%
с3976
 
4.0%
к3307
 
3.4%
Other values (62)40884
41.6%
Thai
ValueCountFrequency (%)
4594
 
5.9%
4527
 
5.8%
4167
 
5.4%
3661
 
4.7%
3609
 
4.7%
3608
 
4.7%
3527
 
4.6%
3300
 
4.3%
3050
 
3.9%
2910
 
3.8%
Other values (58)40442
52.3%
Greek
ValueCountFrequency (%)
α772
 
10.5%
ο535
 
7.3%
ι509
 
6.9%
τ420
 
5.7%
ν404
 
5.5%
ρ319
 
4.3%
ε313
 
4.3%
μ291
 
4.0%
λ285
 
3.9%
ά270
 
3.7%
Other values (56)3231
44.0%
Arabic
ValueCountFrequency (%)
ا335
15.1%
ل242
 
10.9%
ي220
 
9.9%
ن129
 
5.8%
م126
 
5.7%
ب124
 
5.6%
و112
 
5.1%
ر100
 
4.5%
ه72
 
3.2%
ح71
 
3.2%
Other values (28)686
30.9%
Bopomofo
ValueCountFrequency (%)
6
 
12.8%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
Other values (27)27
57.4%
Hebrew
ValueCountFrequency (%)
י10097
12.5%
ו7873
 
9.8%
ה6906
 
8.6%
ל6228
 
7.7%
א4922
 
6.1%
ר4652
 
5.8%
ב4203
 
5.2%
ת3934
 
4.9%
ש3735
 
4.6%
מ3684
 
4.6%
Other values (20)24278
30.2%
Lao
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (18)20
47.6%
Inherited
ValueCountFrequency (%)
205
44.0%
́101
21.7%
45
 
9.7%
̈30
 
6.4%
̃27
 
5.8%
̆17
 
3.6%
̊15
 
3.2%
̧11
 
2.4%
̂10
 
2.1%
̀3
 
0.6%
Other values (2)2
 
0.4%
Tibetan
ValueCountFrequency (%)
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Other values (2)2
16.7%
Georgian
ValueCountFrequency (%)
6
33.3%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Unknown
ValueCountFrequency (%)
4
40.0%
2
20.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11320559
95.3%
None136036
 
1.1%
CJK102995
 
0.9%
Cyrillic98175
 
0.8%
Hebrew80512
 
0.7%
Thai77395
 
0.7%
Katakana29058
 
0.2%
Hiragana24912
 
0.2%
Arabic2218
 
< 0.1%
Punctuation1515
 
< 0.1%
Other values (24)1772
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1635323
 
14.4%
e985846
 
8.7%
a831482
 
7.3%
o622983
 
5.5%
i610106
 
5.4%
n553982
 
4.9%
r519226
 
4.6%
t445938
 
3.9%
l378543
 
3.3%
s361331
 
3.2%
Other values (85)4375799
38.7%
None
ValueCountFrequency (%)
é11484
 
8.4%
ä11361
 
8.4%
á10970
 
8.1%
í8657
 
6.4%
ó8354
 
6.1%
ö7107
 
5.2%
ı6723
 
4.9%
ü6314
 
4.6%
å4343
 
3.2%
ñ3452
 
2.5%
Other values (243)57271
42.1%
Hebrew
ValueCountFrequency (%)
י10097
12.5%
ו7873
 
9.8%
ה6906
 
8.6%
ל6228
 
7.7%
א4922
 
6.1%
ר4652
 
5.8%
ב4203
 
5.2%
ת3934
 
4.9%
ש3735
 
4.6%
מ3684
 
4.6%
Other values (20)24278
30.2%
Cyrillic
ValueCountFrequency (%)
а9712
 
9.9%
о8175
 
8.3%
е7528
 
7.7%
и5574
 
5.7%
н5527
 
5.6%
т4935
 
5.0%
р4330
 
4.4%
л4227
 
4.3%
с3976
 
4.0%
к3307
 
3.4%
Other values (62)40884
41.6%
Thai
ValueCountFrequency (%)
4594
 
5.9%
4527
 
5.8%
4167
 
5.4%
3661
 
4.7%
3609
 
4.7%
3608
 
4.7%
3527
 
4.6%
3300
 
4.3%
3050
 
3.9%
2910
 
3.8%
Other values (58)40442
52.3%
Hiragana
ValueCountFrequency (%)
3311
 
13.3%
1715
 
6.9%
1197
 
4.8%
948
 
3.8%
895
 
3.6%
746
 
3.0%
746
 
3.0%
740
 
3.0%
656
 
2.6%
626
 
2.5%
Other values (72)13332
53.5%
CJK
ValueCountFrequency (%)
2841
 
2.8%
2115
 
2.1%
1986
 
1.9%
1870
 
1.8%
1421
 
1.4%
1366
 
1.3%
1176
 
1.1%
1150
 
1.1%
949
 
0.9%
939
 
0.9%
Other values (3430)87182
84.6%
Katakana
ValueCountFrequency (%)
2825
 
9.7%
2066
 
7.1%
1407
 
4.8%
1257
 
4.3%
1084
 
3.7%
1056
 
3.6%
977
 
3.4%
912
 
3.1%
778
 
2.7%
675
 
2.3%
Other values (76)16021
55.1%
Punctuation
ValueCountFrequency (%)
693
45.7%
247
 
16.3%
183
 
12.1%
162
 
10.7%
115
 
7.6%
32
 
2.1%
18
 
1.2%
16
 
1.1%
14
 
0.9%
11
 
0.7%
Other values (6)24
 
1.6%
Arabic
ValueCountFrequency (%)
ا335
15.1%
ل242
 
10.9%
ي220
 
9.9%
ن129
 
5.8%
م126
 
5.7%
ب124
 
5.6%
و112
 
5.0%
ر100
 
4.5%
ه72
 
3.2%
ح71
 
3.2%
Other values (29)687
31.0%
Diacriticals
ValueCountFrequency (%)
́101
47.0%
̈30
 
14.0%
̃27
 
12.6%
̆17
 
7.9%
̊15
 
7.0%
̧11
 
5.1%
̂10
 
4.7%
̀3
 
1.4%
̦1
 
0.5%
Misc Symbols
ValueCountFrequency (%)
33
44.6%
24
32.4%
7
 
9.5%
2
 
2.7%
2
 
2.7%
1
 
1.4%
1
 
1.4%
1
 
1.4%
1
 
1.4%
1
 
1.4%
Hangul
ValueCountFrequency (%)
32
 
2.8%
26
 
2.3%
26
 
2.3%
23
 
2.0%
22
 
1.9%
22
 
1.9%
20
 
1.7%
19
 
1.7%
19
 
1.7%
19
 
1.7%
Other values (301)920
80.1%
Small Forms
ValueCountFrequency (%)
14
100.0%
Letterlike Symbols
ValueCountFrequency (%)
12
100.0%
Number Forms
ValueCountFrequency (%)
11
52.4%
5
23.8%
3
 
14.3%
1
 
4.8%
1
 
4.8%
Arrows
ValueCountFrequency (%)
10
66.7%
5
33.3%
IPA Ext
ValueCountFrequency (%)
ə7
77.8%
ɪ2
 
22.2%
Currency Symbols
ValueCountFrequency (%)
7
100.0%
Jamo
ValueCountFrequency (%)
7
 
11.5%
7
 
11.5%
6
 
9.8%
4
 
6.6%
4
 
6.6%
3
 
4.9%
3
 
4.9%
3
 
4.9%
3
 
4.9%
3
 
4.9%
Other values (13)18
29.5%
Latin Ext Additional
ValueCountFrequency (%)
ế7
25.9%
3
11.1%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (5)5
18.5%
Georgian
ValueCountFrequency (%)
6
33.3%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Bopomofo
ValueCountFrequency (%)
6
 
12.8%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
Other values (27)27
57.4%
Math Operators
ValueCountFrequency (%)
5
50.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
PUA
ValueCountFrequency (%)
4
40.0%
2
20.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
Specials
ValueCountFrequency (%)
3
100.0%
Geometric Shapes
ValueCountFrequency (%)
3
21.4%
3
21.4%
3
21.4%
3
21.4%
1
 
7.1%
1
 
7.1%
Lao
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (18)20
47.6%
Modifier Letters
ValueCountFrequency (%)
ˈ2
33.3%
˙1
16.7%
ʻ1
16.7%
ˇ1
16.7%
ˋ1
16.7%
CJK Ext B
ValueCountFrequency (%)
𠱁2
100.0%
Tibetan
ValueCountFrequency (%)
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Other values (2)2
16.7%
Sup Math Operators
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
VS
ValueCountFrequency (%)
1
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%

popularity
Real number (ℝ≥0)

ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.57005277
Minimum0
Maximum100
Zeros44690
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:20.984373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median27
Q341
95-th percentile59
Maximum100
Range100
Interquartile range (IQR)28

Descriptive statistics

Standard deviation18.37064237
Coefficient of variation (CV)0.6663259776
Kurtosis-0.6328021142
Mean27.57005277
Median Absolute Deviation (MAD)14
Skewness0.2786970041
Sum16174578
Variance337.4805009
MonotonicityNot monotonic
2022-09-15T01:45:21.104347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044690
 
7.6%
3512231
 
2.1%
2312139
 
2.1%
112024
 
2.0%
3611879
 
2.0%
3411328
 
1.9%
2711292
 
1.9%
2211206
 
1.9%
3311174
 
1.9%
2411148
 
1.9%
Other values (91)437561
74.6%
ValueCountFrequency (%)
044690
7.6%
112024
 
2.0%
29639
 
1.6%
38154
 
1.4%
47733
 
1.3%
57730
 
1.3%
67659
 
1.3%
77726
 
1.3%
87988
 
1.4%
98265
 
1.4%
ValueCountFrequency (%)
1001
 
< 0.1%
991
 
< 0.1%
981
 
< 0.1%
972
 
< 0.1%
962
 
< 0.1%
951
 
< 0.1%
946
< 0.1%
932
 
< 0.1%
9210
< 0.1%
9111
< 0.1%

duration_ms
Real number (ℝ≥0)

Distinct123122
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230051.1673
Minimum3344
Maximum5621218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:21.244272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3344
5-th percentile97307
Q1175093
median214893
Q3263867
95-th percentile382333
Maximum5621218
Range5617874
Interquartile range (IQR)88774

Descriptive statistics

Standard deviation126526.0874
Coefficient of variation (CV)0.549991069
Kurtosis241.0665521
Mean230051.1673
Median Absolute Deviation (MAD)43838.5
Skewness10.32562215
Sum1.349645784 × 1011
Variance1.60088508 × 1010
MonotonicityNot monotonic
2022-09-15T01:45:21.354177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240000215
 
< 0.1%
192000201
 
< 0.1%
180000199
 
< 0.1%
216000184
 
< 0.1%
210000171
 
< 0.1%
184000166
 
< 0.1%
200000166
 
< 0.1%
208000162
 
< 0.1%
228000152
 
< 0.1%
198000151
 
< 0.1%
Other values (123112)584905
99.7%
ValueCountFrequency (%)
33444
< 0.1%
40008
< 0.1%
49371
 
< 0.1%
51081
 
< 0.1%
59911
 
< 0.1%
63601
 
< 0.1%
63621
 
< 0.1%
63733
 
< 0.1%
75231
 
< 0.1%
85941
 
< 0.1%
ValueCountFrequency (%)
56212181
< 0.1%
54035001
< 0.1%
50421851
< 0.1%
49950831
< 0.1%
48643331
< 0.1%
48001181
< 0.1%
47972581
< 0.1%
47925871
< 0.1%
47866721
< 0.1%
47755181
< 0.1%

explicit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
560808 
1
 
25864

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586672
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0560808
95.6%
125864
 
4.4%

Length

2022-09-15T01:45:21.464160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-15T01:45:21.564103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0560808
95.6%
125864
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0560808
95.6%
125864
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number586672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0560808
95.6%
125864
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common586672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0560808
95.6%
125864
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII586672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0560808
95.6%
125864
 
4.4%

artists
Categorical

HIGH CARDINALITY

Distinct114030
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
['Die drei ???']
 
3856
['TKKG Retro-Archiv']
 
2006
['Benjamin Blümchen']
 
1503
['Bibi Blocksberg']
 
1472
['Lata Mangeshkar']
 
1373
Other values (114025)
576462 

Length

Max length934
Median length492
Mean length21.61295579
Min length4

Characters and Unicode

Total characters12679716
Distinct characters2156
Distinct categories20 ?
Distinct scripts13 ?
Distinct blocks22 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66232 ?
Unique (%)11.3%

Sample

1st row['Uli']
2nd row['Fernando Pessoa']
3rd row['Ignacio Corsini']
4th row['Ignacio Corsini']
5th row['Dick Haymes']

Common Values

ValueCountFrequency (%)
['Die drei ???']3856
 
0.7%
['TKKG Retro-Archiv']2006
 
0.3%
['Benjamin Blümchen']1503
 
0.3%
['Bibi Blocksberg']1472
 
0.3%
['Lata Mangeshkar']1373
 
0.2%
['Bibi und Tina']927
 
0.2%
['Tintin', 'Tomas Bolme', 'Bert-Åke Varg']905
 
0.2%
['Francisco Canaro']891
 
0.2%
['Ella Fitzgerald']870
 
0.1%
['Tadeusz Dolega Mostowicz']838
 
0.1%
Other values (114020)572031
97.5%

Length

2022-09-15T01:45:21.674007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the29997
 
1.9%
22820
 
1.5%
orchestra12441
 
0.8%
de10079
 
0.6%
los9267
 
0.6%
die5267
 
0.3%
la4812
 
0.3%
del4260
 
0.3%
john4178
 
0.3%
his4157
 
0.3%
Other values (80592)1451219
93.1%

Most occurring characters

ValueCountFrequency (%)
'1507306
 
11.9%
971828
 
7.7%
a885658
 
7.0%
e763743
 
6.0%
i607358
 
4.8%
]586740
 
4.6%
[586740
 
4.6%
r583100
 
4.6%
n578674
 
4.6%
o557799
 
4.4%
Other values (2146)5050770
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7049614
55.6%
Other Punctuation1762841
 
13.9%
Uppercase Letter1618636
 
12.8%
Space Separator971828
 
7.7%
Close Punctuation587847
 
4.6%
Open Punctuation587845
 
4.6%
Other Letter58839
 
0.5%
Decimal Number19835
 
0.2%
Dash Punctuation14763
 
0.1%
Nonspacing Mark5568
 
< 0.1%
Other values (10)2100
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1888
 
3.2%
1619
 
2.8%
1187
 
2.0%
1120
 
1.9%
1083
 
1.8%
955
 
1.6%
917
 
1.6%
915
 
1.6%
736
 
1.3%
731
 
1.2%
Other values (1740)47688
81.0%
Lowercase Letter
ValueCountFrequency (%)
a885658
12.6%
e763743
10.8%
i607358
 
8.6%
r583100
 
8.3%
n578674
 
8.2%
o557799
 
7.9%
l397378
 
5.6%
s395691
 
5.6%
t328892
 
4.7%
h256752
 
3.6%
Other values (169)1694569
24.0%
Uppercase Letter
ValueCountFrequency (%)
S138185
 
8.5%
M122570
 
7.6%
B114524
 
7.1%
C102008
 
6.3%
A100915
 
6.2%
T98618
 
6.1%
D83448
 
5.2%
L82798
 
5.1%
R79486
 
4.9%
P76285
 
4.7%
Other values (116)619799
38.3%
Other Punctuation
ValueCountFrequency (%)
'1507306
85.5%
,173449
 
9.8%
.31571
 
1.8%
&17636
 
1.0%
"17113
 
1.0%
?11659
 
0.7%
/1513
 
0.1%
!1455
 
0.1%
:250
 
< 0.1%
233
 
< 0.1%
Other values (16)656
 
< 0.1%
Nonspacing Mark
ValueCountFrequency (%)
1404
25.2%
1062
19.1%
769
13.8%
573
10.3%
458
 
8.2%
391
 
7.0%
280
 
5.0%
262
 
4.7%
141
 
2.5%
98
 
1.8%
Other values (4)130
 
2.3%
Decimal Number
ValueCountFrequency (%)
13345
16.9%
23192
16.1%
02836
14.3%
41944
9.8%
31825
9.2%
51705
8.6%
71357
6.8%
91310
 
6.6%
81211
 
6.1%
61110
 
5.6%
Math Symbol
ValueCountFrequency (%)
+186
78.8%
|25
 
10.6%
=12
 
5.1%
×3
 
1.3%
~3
 
1.3%
>2
 
0.8%
2
 
0.8%
1
 
0.4%
<1
 
0.4%
1
 
0.4%
Modifier Symbol
ValueCountFrequency (%)
´69
69.7%
`21
 
21.2%
^4
 
4.0%
¨3
 
3.0%
¯1
 
1.0%
1
 
1.0%
Other Symbol
ValueCountFrequency (%)
21
48.8%
°10
23.3%
5
 
11.6%
®4
 
9.3%
2
 
4.7%
©1
 
2.3%
Currency Symbol
ValueCountFrequency (%)
$456
96.6%
¥11
 
2.3%
3
 
0.6%
¢1
 
0.2%
£1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
]586740
99.8%
)1090
 
0.2%
10
 
< 0.1%
7
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
[586740
99.8%
(1087
 
0.2%
10
 
< 0.1%
8
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-14649
99.2%
110
 
0.7%
4
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
156
63.7%
»56
 
22.9%
33
 
13.5%
Initial Punctuation
ValueCountFrequency (%)
«55
80.9%
10
 
14.7%
3
 
4.4%
Modifier Letter
ValueCountFrequency (%)
869
99.2%
7
 
0.8%
Other Number
ValueCountFrequency (%)
²3
75.0%
³1
 
25.0%
Space Separator
ValueCountFrequency (%)
971828
100.0%
Connector Punctuation
ValueCountFrequency (%)
_56
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8626881
68.0%
Common3947045
31.1%
Cyrillic35174
 
0.3%
Han30733
 
0.2%
Thai24963
 
0.2%
Greek6209
 
< 0.1%
Katakana5238
 
< 0.1%
Hebrew1830
 
< 0.1%
Hiragana1050
 
< 0.1%
Arabic467
 
< 0.1%
Other values (3)126
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
731
 
2.4%
505
 
1.6%
403
 
1.3%
396
 
1.3%
392
 
1.3%
387
 
1.3%
376
 
1.2%
357
 
1.2%
325
 
1.1%
314
 
1.0%
Other values (1430)26547
86.4%
Latin
ValueCountFrequency (%)
a885658
 
10.3%
e763743
 
8.9%
i607358
 
7.0%
r583100
 
6.8%
n578674
 
6.7%
o557799
 
6.5%
l397378
 
4.6%
s395691
 
4.6%
t328892
 
3.8%
h256752
 
3.0%
Other values (166)3271836
37.9%
Common
ValueCountFrequency (%)
'1507306
38.2%
971828
24.6%
]586740
 
14.9%
[586740
 
14.9%
,173449
 
4.4%
.31571
 
0.8%
&17636
 
0.4%
"17113
 
0.4%
-14649
 
0.4%
?11659
 
0.3%
Other values (75)28354
 
0.7%
Katakana
ValueCountFrequency (%)
484
 
9.2%
447
 
8.5%
422
 
8.1%
385
 
7.4%
341
 
6.5%
334
 
6.4%
332
 
6.3%
306
 
5.8%
127
 
2.4%
104
 
2.0%
Other values (70)1956
37.3%
Cyrillic
ValueCountFrequency (%)
а3565
 
10.1%
и2858
 
8.1%
о2645
 
7.5%
н2624
 
7.5%
е2133
 
6.1%
р2083
 
5.9%
в1795
 
5.1%
л1487
 
4.2%
с1284
 
3.7%
т1263
 
3.6%
Other values (58)13437
38.2%
Hiragana
ValueCountFrequency (%)
62
 
5.9%
61
 
5.8%
58
 
5.5%
58
 
5.5%
57
 
5.4%
39
 
3.7%
39
 
3.7%
35
 
3.3%
33
 
3.1%
32
 
3.0%
Other values (54)576
54.9%
Thai
ValueCountFrequency (%)
1888
 
7.6%
1619
 
6.5%
1404
 
5.6%
1187
 
4.8%
1120
 
4.5%
1083
 
4.3%
1062
 
4.3%
955
 
3.8%
917
 
3.7%
915
 
3.7%
Other values (52)12813
51.3%
Hangul
ValueCountFrequency (%)
8
 
7.3%
6
 
5.5%
5
 
4.6%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
Other values (51)69
63.3%
Greek
ValueCountFrequency (%)
ς562
 
9.1%
α514
 
8.3%
ο418
 
6.7%
ρ362
 
5.8%
τ361
 
5.8%
η310
 
5.0%
ν250
 
4.0%
ι233
 
3.8%
λ213
 
3.4%
κ213
 
3.4%
Other values (44)2773
44.7%
Hebrew
ValueCountFrequency (%)
י314
17.2%
ו195
10.7%
ר144
 
7.9%
ה123
 
6.7%
ב112
 
6.1%
נ107
 
5.8%
א107
 
5.8%
ל104
 
5.7%
ן82
 
4.5%
מ57
 
3.1%
Other values (18)485
26.5%
Arabic
ValueCountFrequency (%)
م51
10.9%
ي49
 
10.5%
ا37
 
7.9%
د34
 
7.3%
ل34
 
7.3%
ز33
 
7.1%
ف31
 
6.6%
ر26
 
5.6%
ح25
 
5.4%
و22
 
4.7%
Other values (18)125
26.8%
Georgian
ValueCountFrequency (%)
4
28.6%
3
21.4%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Inherited
ValueCountFrequency (%)
́3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12482277
98.4%
None96402
 
0.8%
Cyrillic35174
 
0.3%
CJK30717
 
0.2%
Thai24963
 
0.2%
Katakana6340
 
0.1%
Hebrew1830
 
< 0.1%
Hiragana1051
 
< 0.1%
Arabic467
 
< 0.1%
Punctuation316
 
< 0.1%
Other values (12)179
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'1507306
 
12.1%
971828
 
7.8%
a885658
 
7.1%
e763743
 
6.1%
i607358
 
4.9%
]586740
 
4.7%
[586740
 
4.7%
r583100
 
4.7%
n578674
 
4.6%
o557799
 
4.5%
Other values (83)4853331
38.9%
None
ValueCountFrequency (%)
é14064
 
14.6%
á9701
 
10.1%
ü7827
 
8.1%
ó6875
 
7.1%
í6494
 
6.7%
ö5330
 
5.5%
ı2911
 
3.0%
ä2518
 
2.6%
ç2191
 
2.3%
ú2179
 
2.3%
Other values (192)36312
37.7%
Cyrillic
ValueCountFrequency (%)
а3565
 
10.1%
и2858
 
8.1%
о2645
 
7.5%
н2624
 
7.5%
е2133
 
6.1%
р2083
 
5.9%
в1795
 
5.1%
л1487
 
4.2%
с1284
 
3.7%
т1263
 
3.6%
Other values (58)13437
38.2%
Thai
ValueCountFrequency (%)
1888
 
7.6%
1619
 
6.5%
1404
 
5.6%
1187
 
4.8%
1120
 
4.5%
1083
 
4.3%
1062
 
4.3%
955
 
3.8%
917
 
3.7%
915
 
3.7%
Other values (52)12813
51.3%
Katakana
ValueCountFrequency (%)
869
 
13.7%
484
 
7.6%
447
 
7.1%
422
 
6.7%
385
 
6.1%
341
 
5.4%
334
 
5.3%
332
 
5.2%
306
 
4.8%
233
 
3.7%
Other values (72)2187
34.5%
CJK
ValueCountFrequency (%)
731
 
2.4%
505
 
1.6%
403
 
1.3%
396
 
1.3%
392
 
1.3%
387
 
1.3%
376
 
1.2%
357
 
1.2%
325
 
1.1%
314
 
1.0%
Other values (1427)26531
86.4%
Hebrew
ValueCountFrequency (%)
י314
17.2%
ו195
10.7%
ר144
 
7.9%
ה123
 
6.7%
ב112
 
6.1%
נ107
 
5.8%
א107
 
5.8%
ל104
 
5.7%
ן82
 
4.5%
מ57
 
3.1%
Other values (18)485
26.5%
Punctuation
ValueCountFrequency (%)
156
49.4%
110
34.8%
33
 
10.4%
10
 
3.2%
3
 
0.9%
3
 
0.9%
1
 
0.3%
Hiragana
ValueCountFrequency (%)
62
 
5.9%
61
 
5.8%
58
 
5.5%
58
 
5.5%
57
 
5.4%
39
 
3.7%
39
 
3.7%
35
 
3.3%
33
 
3.1%
32
 
3.0%
Other values (55)577
54.9%
Arabic
ValueCountFrequency (%)
م51
10.9%
ي49
 
10.5%
ا37
 
7.9%
د34
 
7.3%
ل34
 
7.3%
ز33
 
7.1%
ف31
 
6.6%
ر26
 
5.6%
ح25
 
5.4%
و22
 
4.7%
Other values (18)125
26.8%
Misc Symbols
ValueCountFrequency (%)
21
91.3%
2
 
8.7%
Hangul
ValueCountFrequency (%)
8
 
7.3%
6
 
5.5%
5
 
4.6%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
Other values (51)69
63.3%
CJK Compat Ideographs
ValueCountFrequency (%)
7
100.0%
Latin Ext Additional
ValueCountFrequency (%)
6
75.0%
1
 
12.5%
1
 
12.5%
Letterlike Symbols
ValueCountFrequency (%)
5
100.0%
Georgian
ValueCountFrequency (%)
4
28.6%
3
21.4%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Currency Symbols
ValueCountFrequency (%)
3
100.0%
Diacriticals
ValueCountFrequency (%)
́3
100.0%
CJK Ext B
ValueCountFrequency (%)
𤒹2
100.0%
Math Operators
ValueCountFrequency (%)
2
66.7%
1
33.3%
Arrows
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%

id_artists
Categorical

HIGH CARDINALITY

Distinct115062
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
['3meJIgRw7YleJrmbpbJK6S']
 
3856
['0i38tQX5j4gZ0KS3eCMoIl']
 
2006
['1l6d0RIxTL3JytlLGvWzYe']
 
1503
['3t2iKODSDyzoDJw7AsD99u']
 
1472
['61JrslREXq98hurYL2hYoc']
 
1373
Other values (115057)
576462 

Length

Max length1508
Median length26
Mean length33.55609267
Min length26

Characters and Unicode

Total characters19686420
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67145 ?
Unique (%)11.4%

Sample

1st row['45tIt06XoI0Iio4LBEVpls']
2nd row['14jtPCOoNZwquk5wd9DxrY']
3rd row['5LiOoJbxVSAMkBS2fUm3X2']
4th row['5LiOoJbxVSAMkBS2fUm3X2']
5th row['3BiJGZsyX9sJchTqcSA7Su']

Common Values

ValueCountFrequency (%)
['3meJIgRw7YleJrmbpbJK6S']3856
 
0.7%
['0i38tQX5j4gZ0KS3eCMoIl']2006
 
0.3%
['1l6d0RIxTL3JytlLGvWzYe']1503
 
0.3%
['3t2iKODSDyzoDJw7AsD99u']1472
 
0.3%
['61JrslREXq98hurYL2hYoc']1373
 
0.2%
['2x8vG4f0HYXzMEo3xNsoiI']927
 
0.2%
['6aMD1KAa5i3Myy61cR8FiW', '7HjbJ8V87zrxkSzL1KieQk', '71ADe4Zg9UyE8WQEHbJSXM']905
 
0.2%
['2maQMqxNnlRrBrS1oAsrX9']891
 
0.2%
['5V0MlUE1Bft0mbLlND7FJz']870
 
0.1%
['4eeMulNeqpZGBxybCxZOdC']838
 
0.1%
Other values (115052)572031
97.5%

Length

2022-09-15T01:45:21.803938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3mejigrw7ylejrmbpbjk6s3856
 
0.5%
61jrslrexq98huryl2hyoc2605
 
0.3%
5aiqb5nvvvmfsvsdexz4082020
 
0.3%
2maqmqxnnlrrbrs1oasrx92010
 
0.3%
0i38tqx5j4gz0ks3ecmoil2006
 
0.3%
4njhfmfw43rlbljqvxdurs1821
 
0.2%
0gxdpqwyndodn7fb0rdn8j1553
 
0.2%
1l6d0rixtl3jytllgvwzye1503
 
0.2%
3t2ikodsdyzodjw7asd99u1472
 
0.2%
2woqmjp9tyabvthdosotus1253
 
0.2%
Other values (98494)737071
97.3%

Most occurring characters

ValueCountFrequency (%)
'1514340
 
7.7%
[586672
 
3.0%
]586672
 
3.0%
0365662
 
1.9%
4359536
 
1.8%
2358258
 
1.8%
5358255
 
1.8%
3353126
 
1.8%
1350394
 
1.8%
6344241
 
1.7%
Other values (57)14509264
73.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6705473
34.1%
Uppercase Letter6611755
33.6%
Decimal Number3340512
17.0%
Other Punctuation1684838
 
8.6%
Open Punctuation586672
 
3.0%
Close Punctuation586672
 
3.0%
Space Separator170498
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e275758
 
4.1%
l267655
 
4.0%
m266604
 
4.0%
b264203
 
3.9%
y263309
 
3.9%
x263030
 
3.9%
r262848
 
3.9%
s262422
 
3.9%
q261029
 
3.9%
o260528
 
3.9%
Other values (16)4058087
60.5%
Uppercase Letter
ValueCountFrequency (%)
S267989
 
4.1%
D267159
 
4.0%
J266786
 
4.0%
C261777
 
4.0%
X261442
 
4.0%
R259970
 
3.9%
Y259593
 
3.9%
O257776
 
3.9%
L254702
 
3.9%
I254611
 
3.9%
Other values (16)3999950
60.5%
Decimal Number
ValueCountFrequency (%)
0365662
10.9%
4359536
10.8%
2358258
10.7%
5358255
10.7%
3353126
10.6%
1350394
10.5%
6344241
10.3%
7335002
10.0%
8261694
7.8%
9254344
7.6%
Other Punctuation
ValueCountFrequency (%)
'1514340
89.9%
,170498
 
10.1%
Open Punctuation
ValueCountFrequency (%)
[586672
100.0%
Close Punctuation
ValueCountFrequency (%)
]586672
100.0%
Space Separator
ValueCountFrequency (%)
170498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13317228
67.6%
Common6369192
32.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e275758
 
2.1%
S267989
 
2.0%
l267655
 
2.0%
D267159
 
2.0%
J266786
 
2.0%
m266604
 
2.0%
b264203
 
2.0%
y263309
 
2.0%
x263030
 
2.0%
r262848
 
2.0%
Other values (42)10651887
80.0%
Common
ValueCountFrequency (%)
'1514340
23.8%
[586672
 
9.2%
]586672
 
9.2%
0365662
 
5.7%
4359536
 
5.6%
2358258
 
5.6%
5358255
 
5.6%
3353126
 
5.5%
1350394
 
5.5%
6344241
 
5.4%
Other values (5)1192036
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII19686420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'1514340
 
7.7%
[586672
 
3.0%
]586672
 
3.0%
0365662
 
1.9%
4359536
 
1.8%
2358258
 
1.8%
5358255
 
1.8%
3353126
 
1.8%
1350394
 
1.8%
6344241
 
1.7%
Other values (57)14509264
73.7%

release_date
Categorical

HIGH CARDINALITY

Distinct19700
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
1998-01-01
 
2893
1997-01-01
 
2892
1995
 
2871
1997
 
2811
1996
 
2776
Other values (19695)
572429 

Length

Max length10
Median length10
Mean length8.593353697
Min length4

Characters and Unicode

Total characters5041480
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2176 ?
Unique (%)0.4%

Sample

1st row1922-02-22
2nd row1922-06-01
3rd row1922-03-21
4th row1922-03-21
5th row1922

Common Values

ValueCountFrequency (%)
1998-01-012893
 
0.5%
1997-01-012892
 
0.5%
19952871
 
0.5%
19972811
 
0.5%
19962776
 
0.5%
1990-01-012752
 
0.5%
19982726
 
0.5%
1996-01-012705
 
0.5%
19942611
 
0.4%
1995-01-012575
 
0.4%
Other values (19690)559060
95.3%

Length

2022-09-15T01:45:21.913918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1998-01-012893
 
0.5%
1997-01-012892
 
0.5%
19952871
 
0.5%
19972811
 
0.5%
19962776
 
0.5%
1990-01-012752
 
0.5%
19982726
 
0.5%
1996-01-012705
 
0.5%
19942611
 
0.4%
1995-01-012575
 
0.4%
Other values (19690)559060
95.3%

Most occurring characters

ValueCountFrequency (%)
11100398
21.8%
01000347
19.8%
-898264
17.8%
9604970
12.0%
2479776
9.5%
8194866
 
3.9%
7173992
 
3.5%
6162608
 
3.2%
5153465
 
3.0%
3143262
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4143216
82.2%
Dash Punctuation898264
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11100398
26.6%
01000347
24.1%
9604970
14.6%
2479776
11.6%
8194866
 
4.7%
7173992
 
4.2%
6162608
 
3.9%
5153465
 
3.7%
3143262
 
3.5%
4129532
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
-898264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5041480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11100398
21.8%
01000347
19.8%
-898264
17.8%
9604970
12.0%
2479776
9.5%
8194866
 
3.9%
7173992
 
3.5%
6162608
 
3.2%
5153465
 
3.0%
3143262
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5041480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11100398
21.8%
01000347
19.8%
-898264
17.8%
9604970
12.0%
2479776
9.5%
8194866
 
3.9%
7173992
 
3.5%
6162608
 
3.2%
5153465
 
3.0%
3143262
 
2.8%

danceability
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1285
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.563593818
Minimum0
Maximum0.991
Zeros328
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:22.013865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.267
Q10.453
median0.577
Q30.686
95-th percentile0.815
Maximum0.991
Range0.991
Interquartile range (IQR)0.233

Descriptive statistics

Standard deviation0.1661026543
Coefficient of variation (CV)0.2947205044
Kurtosis-0.2740209589
Mean0.563593818
Median Absolute Deviation (MAD)0.115
Skewness-0.3308254393
Sum330644.7124
Variance0.02759009176
MonotonicityNot monotonic
2022-09-15T01:45:22.133801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6371483
 
0.3%
0.6291453
 
0.2%
0.6021446
 
0.2%
0.5951444
 
0.2%
0.6161442
 
0.2%
0.631440
 
0.2%
0.621437
 
0.2%
0.6321433
 
0.2%
0.6071431
 
0.2%
0.5651429
 
0.2%
Other values (1275)572234
97.5%
ValueCountFrequency (%)
0328
0.1%
0.05321
 
< 0.1%
0.05461
 
< 0.1%
0.05592
 
< 0.1%
0.05621
 
< 0.1%
0.05692
 
< 0.1%
0.0571
 
< 0.1%
0.05721
 
< 0.1%
0.05742
 
< 0.1%
0.05791
 
< 0.1%
ValueCountFrequency (%)
0.9911
 
< 0.1%
0.9883
< 0.1%
0.9872
 
< 0.1%
0.9863
< 0.1%
0.9856
< 0.1%
0.9845
< 0.1%
0.9832
 
< 0.1%
0.9824
< 0.1%
0.9811
 
< 0.1%
0.987
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2571
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5420359919
Minimum0
Maximum1
Zeros33
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:22.253737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.343
median0.549
Q30.748
95-th percentile0.931
Maximum1
Range1
Interquartile range (IQR)0.405

Descriptive statistics

Standard deviation0.2519229409
Coefficient of variation (CV)0.4647716105
Kurtosis-0.9637915654
Mean0.5420359919
Median Absolute Deviation (MAD)0.202
Skewness-0.1313828157
Sum317997.3394
Variance0.06346516815
MonotonicityNot monotonic
2022-09-15T01:45:22.373674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.526847
 
0.1%
0.538846
 
0.1%
0.716836
 
0.1%
0.448835
 
0.1%
0.497832
 
0.1%
0.534826
 
0.1%
0.53826
 
0.1%
0.666823
 
0.1%
0.726821
 
0.1%
0.499820
 
0.1%
Other values (2561)578360
98.6%
ValueCountFrequency (%)
033
< 0.1%
1.97 × 10-52
 
< 0.1%
1.98 × 10-51
 
< 0.1%
1.99 × 10-52
 
< 0.1%
2 × 10-53
 
< 0.1%
2.01 × 10-510
 
< 0.1%
2.02 × 10-512
 
< 0.1%
2.03 × 10-536
< 0.1%
2.8 × 10-51
 
< 0.1%
3.05 × 10-51
 
< 0.1%
ValueCountFrequency (%)
164
 
< 0.1%
0.999217
< 0.1%
0.998223
< 0.1%
0.997245
< 0.1%
0.996255
< 0.1%
0.995312
0.1%
0.994267
< 0.1%
0.993256
< 0.1%
0.992262
< 0.1%
0.991311
0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.221602531
Minimum0
Maximum11
Zeros74950
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:22.483615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.519423109
Coefficient of variation (CV)0.6740120658
Kurtosis-1.265939488
Mean5.221602531
Median Absolute Deviation (MAD)3
Skewness-0.001393638612
Sum3063368
Variance12.38633902
MonotonicityNot monotonic
2022-09-15T01:45:22.563534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
074950
12.8%
773779
12.6%
266552
11.3%
965128
11.1%
553614
9.1%
448220
8.2%
141736
7.1%
1139132
6.7%
1037710
6.4%
833460
5.7%
Other values (2)52391
8.9%
ValueCountFrequency (%)
074950
12.8%
141736
7.1%
266552
11.3%
321535
 
3.7%
448220
8.2%
553614
9.1%
630856
5.3%
773779
12.6%
833460
5.7%
965128
11.1%
ValueCountFrequency (%)
1139132
6.7%
1037710
6.4%
965128
11.1%
833460
5.7%
773779
12.6%
630856
5.3%
553614
9.1%
448220
8.2%
321535
 
3.7%
266552
11.3%

loudness
Real number (ℝ)

HIGH CORRELATION

Distinct29196
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.20606681
Minimum-60
Maximum5.376
Zeros0
Zeros (%)0.0%
Negative586453
Negative (%)> 99.9%
Memory size4.5 MiB
2022-09-15T01:45:22.663519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-19.843
Q1-12.891
median-9.243
Q3-6.482
95-th percentile-3.91
Maximum5.376
Range65.376
Interquartile range (IQR)6.409

Descriptive statistics

Standard deviation5.089327902
Coefficient of variation (CV)-0.498657122
Kurtosis2.717572143
Mean-10.20606681
Median Absolute Deviation (MAD)3.095
Skewness-1.235983362
Sum-5987613.627
Variance25.90125849
MonotonicityNot monotonic
2022-09-15T01:45:22.783456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-8.026116
 
< 0.1%
-5.79795
 
< 0.1%
-4.4795
 
< 0.1%
-5.58481
 
< 0.1%
-7.34880
 
< 0.1%
-6.48479
 
< 0.1%
-7.03178
 
< 0.1%
-7.01678
 
< 0.1%
-8.87178
 
< 0.1%
-6.65178
 
< 0.1%
Other values (29186)585814
99.9%
ValueCountFrequency (%)
-6027
< 0.1%
-57.0931
 
< 0.1%
-551
 
< 0.1%
-54.8371
 
< 0.1%
-54.3761
 
< 0.1%
-53.9861
 
< 0.1%
-53.5981
 
< 0.1%
-51.81
 
< 0.1%
-50.1741
 
< 0.1%
-49.3281
 
< 0.1%
ValueCountFrequency (%)
5.3761
< 0.1%
5.1091
< 0.1%
4.5841
< 0.1%
4.3621
< 0.1%
4.111
< 0.1%
3.8551
< 0.1%
3.7441
< 0.1%
3.5751
< 0.1%
3.4981
< 0.1%
3.2731
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
1
386498 
0
200174 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586672
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1386498
65.9%
0200174
34.1%

Length

2022-09-15T01:45:22.893358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-15T01:45:22.973356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1386498
65.9%
0200174
34.1%

Most occurring characters

ValueCountFrequency (%)
1386498
65.9%
0200174
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number586672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1386498
65.9%
0200174
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common586672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1386498
65.9%
0200174
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII586672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1386498
65.9%
0200174
34.1%

speechiness
Real number (ℝ≥0)

Distinct1655
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1048635418
Minimum0
Maximum0.971
Zeros329
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:23.073299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0276
Q10.034
median0.0443
Q30.0763
95-th percentile0.422
Maximum0.971
Range0.971
Interquartile range (IQR)0.0423

Descriptive statistics

Standard deviation0.1798927924
Coefficient of variation (CV)1.715494149
Kurtosis13.41744916
Mean0.1048635418
Median Absolute Deviation (MAD)0.0133
Skewness3.693950551
Sum61520.5038
Variance0.03236141676
MonotonicityNot monotonic
2022-09-15T01:45:23.193237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03122002
 
0.3%
0.0331997
 
0.3%
0.03321990
 
0.3%
0.03081990
 
0.3%
0.03241979
 
0.3%
0.03091974
 
0.3%
0.03261973
 
0.3%
0.03191972
 
0.3%
0.03111970
 
0.3%
0.03131962
 
0.3%
Other values (1645)566863
96.6%
ValueCountFrequency (%)
0329
0.1%
0.02162
 
< 0.1%
0.02182
 
< 0.1%
0.0222
 
< 0.1%
0.02216
 
< 0.1%
0.02227
 
< 0.1%
0.022317
 
< 0.1%
0.022410
 
< 0.1%
0.022518
 
< 0.1%
0.022618
 
< 0.1%
ValueCountFrequency (%)
0.9713
 
< 0.1%
0.977
 
< 0.1%
0.96925
 
< 0.1%
0.96837
 
< 0.1%
0.96741
 
< 0.1%
0.96692
 
< 0.1%
0.965117
< 0.1%
0.964161
< 0.1%
0.963230
< 0.1%
0.962249
< 0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5217
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4498627244
Minimum0
Maximum0.996
Zeros66
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:23.313185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00177
Q10.0969
median0.422
Q30.785
95-th percentile0.983
Maximum0.996
Range0.996
Interquartile range (IQR)0.6881

Descriptive statistics

Standard deviation0.3488367001
Coefficient of variation (CV)0.7754292168
Kurtosis-1.466174286
Mean0.4498627244
Median Absolute Deviation (MAD)0.3403
Skewness0.1511610482
Sum263921.8643
Variance0.1216870433
MonotonicityNot monotonic
2022-09-15T01:45:23.423114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9954610
 
0.8%
0.9943574
 
0.6%
0.9932913
 
0.5%
0.9922490
 
0.4%
0.9912320
 
0.4%
0.992091
 
0.4%
0.9891916
 
0.3%
0.9881685
 
0.3%
0.9871581
 
0.3%
0.9961575
 
0.3%
Other values (5207)561917
95.8%
ValueCountFrequency (%)
066
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-63
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-62
 
< 0.1%
1.05 × 10-62
 
< 0.1%
1.06 × 10-62
 
< 0.1%
1.07 × 10-63
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.9961575
 
0.3%
0.9954610
0.8%
0.9943574
0.6%
0.9932913
0.5%
0.9922490
0.4%
0.9912320
0.4%
0.992091
0.4%
0.9891916
0.3%
0.9881685
 
0.3%
0.9871581
 
0.3%

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct5402
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1134507822
Minimum0
Maximum1
Zeros205083
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:23.553055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.45 × 10-5
Q30.00955
95-th percentile0.874
Maximum1
Range1
Interquartile range (IQR)0.00955

Descriptive statistics

Standard deviation0.2668678705
Coefficient of variation (CV)2.352278806
Kurtosis3.547210249
Mean0.1134507822
Median Absolute Deviation (MAD)2.45 × 10-5
Skewness2.270398281
Sum66558.39729
Variance0.0712184603
MonotonicityNot monotonic
2022-09-15T01:45:23.672980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0205083
35.0%
0.911410
 
0.1%
0.904402
 
0.1%
0.916402
 
0.1%
0.905399
 
0.1%
0.917399
 
0.1%
0.901396
 
0.1%
0.912396
 
0.1%
0.888392
 
0.1%
0.897387
 
0.1%
Other values (5392)378006
64.4%
ValueCountFrequency (%)
0205083
35.0%
1 × 10-6140
 
< 0.1%
1.01 × 10-6261
 
< 0.1%
1.02 × 10-6257
 
< 0.1%
1.03 × 10-6272
 
< 0.1%
1.04 × 10-6271
 
< 0.1%
1.05 × 10-6241
 
< 0.1%
1.06 × 10-6231
 
< 0.1%
1.07 × 10-6262
 
< 0.1%
1.08 × 10-6239
 
< 0.1%
ValueCountFrequency (%)
122
< 0.1%
0.99917
< 0.1%
0.9989
 
< 0.1%
0.99715
< 0.1%
0.99611
< 0.1%
0.99514
< 0.1%
0.99418
< 0.1%
0.99322
< 0.1%
0.99221
< 0.1%
0.99123
< 0.1%

liveness
Real number (ℝ≥0)

Distinct1782
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2139350169
Minimum0
Maximum1
Zeros43
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:23.792882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0589
Q10.0983
median0.139
Q30.278
95-th percentile0.653
Maximum1
Range1
Interquartile range (IQR)0.1797

Descriptive statistics

Standard deviation0.1843255981
Coefficient of variation (CV)0.8615962026
Kurtosis4.288780693
Mean0.2139350169
Median Absolute Deviation (MAD)0.058
Skewness2.044802293
Sum125509.6842
Variance0.03397592613
MonotonicityNot monotonic
2022-09-15T01:45:23.912861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1115579
 
1.0%
0.115310
 
0.9%
0.1095173
 
0.9%
0.1085162
 
0.9%
0.1074946
 
0.8%
0.1124834
 
0.8%
0.1064788
 
0.8%
0.1054674
 
0.8%
0.1044592
 
0.8%
0.1034442
 
0.8%
Other values (1772)537172
91.6%
ValueCountFrequency (%)
043
< 0.1%
0.005721
 
< 0.1%
0.008381
 
< 0.1%
0.009671
 
< 0.1%
0.009861
 
< 0.1%
0.009891
 
< 0.1%
0.01011
 
< 0.1%
0.01082
 
< 0.1%
0.01112
 
< 0.1%
0.01123
 
< 0.1%
ValueCountFrequency (%)
14
 
< 0.1%
0.9994
 
< 0.1%
0.9984
 
< 0.1%
0.99713
 
< 0.1%
0.99612
 
< 0.1%
0.99517
 
< 0.1%
0.99421
< 0.1%
0.99319
< 0.1%
0.99233
< 0.1%
0.99146
< 0.1%

valence
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1805
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5522924731
Minimum0
Maximum1
Zeros369
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:24.032788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.121
Q10.346
median0.564
Q30.769
95-th percentile0.946
Maximum1
Range1
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.257670937
Coefficient of variation (CV)0.4665479788
Kurtosis-1.037216421
Mean0.5522924731
Median Absolute Deviation (MAD)0.211
Skewness-0.1523059538
Sum324014.5298
Variance0.06639431178
MonotonicityNot monotonic
2022-09-15T01:45:24.152726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9612679
 
0.5%
0.9622312
 
0.4%
0.9632023
 
0.3%
0.9641846
 
0.3%
0.961651
 
0.3%
0.9651599
 
0.3%
0.9661489
 
0.3%
0.9671349
 
0.2%
0.9681155
 
0.2%
0.969948
 
0.2%
Other values (1795)569621
97.1%
ValueCountFrequency (%)
0369
0.1%
1 × 10-5108
 
< 0.1%
6.41 × 10-51
 
< 0.1%
0.0001831
 
< 0.1%
0.0005621
 
< 0.1%
0.0009981
 
< 0.1%
0.001231
 
< 0.1%
0.001281
 
< 0.1%
0.001421
 
< 0.1%
0.001551
 
< 0.1%
ValueCountFrequency (%)
114
< 0.1%
0.9993
 
< 0.1%
0.9975
 
< 0.1%
0.9967
 
< 0.1%
0.9956
 
< 0.1%
0.99412
< 0.1%
0.9935
 
< 0.1%
0.99215
< 0.1%
0.99119
< 0.1%
0.9927
< 0.1%

tempo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct122706
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.4648566
Minimum0
Maximum246.381
Zeros328
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2022-09-15T01:45:24.262669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.92355
Q195.6
median117.384
Q3136.321
95-th percentile174.00845
Maximum246.381
Range246.381
Interquartile range (IQR)40.721

Descriptive statistics

Standard deviation29.7641077
Coefficient of variation (CV)0.251248417
Kurtosis-0.06396733269
Mean118.4648566
Median Absolute Deviation (MAD)20.601
Skewness0.403266268
Sum69500014.37
Variance885.9021073
MonotonicityNot monotonic
2022-09-15T01:45:24.392561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0328
 
0.1%
128.00398
 
< 0.1%
119.99491
 
< 0.1%
139.9889
 
< 0.1%
127.99486
 
< 0.1%
127.99785
 
< 0.1%
128.0182
 
< 0.1%
119.99382
 
< 0.1%
12081
 
< 0.1%
127.99981
 
< 0.1%
Other values (122696)585569
99.8%
ValueCountFrequency (%)
0328
0.1%
30.5061
 
< 0.1%
30.9461
 
< 0.1%
31.211
 
< 0.1%
31.2621
 
< 0.1%
31.291
 
< 0.1%
31.691
 
< 0.1%
31.9881
 
< 0.1%
32.1631
 
< 0.1%
32.2051
 
< 0.1%
ValueCountFrequency (%)
246.3811
< 0.1%
243.7591
< 0.1%
243.5071
< 0.1%
243.3721
< 0.1%
240.7821
< 0.1%
239.9061
< 0.1%
238.8951
< 0.1%
236.7991
< 0.1%
236.1341
< 0.1%
233.0131
< 0.1%

time_signature
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
4
503808 
3
64523 
5
 
11400
1
 
6604
0
 
337

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586672
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row5
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4503808
85.9%
364523
 
11.0%
511400
 
1.9%
16604
 
1.1%
0337
 
0.1%

Length

2022-09-15T01:45:24.492549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-15T01:45:24.582459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4503808
85.9%
364523
 
11.0%
511400
 
1.9%
16604
 
1.1%
0337
 
0.1%

Most occurring characters

ValueCountFrequency (%)
4503808
85.9%
364523
 
11.0%
511400
 
1.9%
16604
 
1.1%
0337
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number586672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4503808
85.9%
364523
 
11.0%
511400
 
1.9%
16604
 
1.1%
0337
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common586672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4503808
85.9%
364523
 
11.0%
511400
 
1.9%
16604
 
1.1%
0337
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII586672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4503808
85.9%
364523
 
11.0%
511400
 
1.9%
16604
 
1.1%
0337
 
0.1%

Interactions

2022-09-15T01:45:13.255434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:36.373708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:39.612968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:42.855937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:46.355497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:49.675086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:53.031258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:56.370859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:59.863713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:03.290045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:06.479565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:09.845850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:13.525403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:36.683304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:39.869633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:43.125880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:46.632139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:49.948427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:53.304572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:56.637463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:00.140427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:03.543334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:06.752877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:10.122529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:13.805342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:36.957067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:40.139583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:43.412524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:46.915409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:50.234950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:53.591221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:56.917466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:00.440386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:03.809883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:07.039489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:10.405745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:14.095276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:37.223292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:40.416186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:43.685790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:47.185353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:50.524957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:53.874570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:57.187814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:00.767059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:04.079918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:07.316267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:10.725714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:14.361856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:37.493271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:40.709547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:44.135724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:47.455283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:50.824925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:54.151133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:57.470682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:01.057052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:04.343155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:07.596125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:11.022377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:14.638963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:37.756559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:40.989510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:44.405682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:47.735284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:51.101522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:54.421157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:57.737356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:01.343518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:04.609864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:07.876199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:11.305654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:14.931792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:38.016556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:41.259510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:44.682446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:48.015262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:51.381491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:54.707777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:58.203965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:01.623551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:04.879842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:08.162751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:11.595609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:15.211824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:38.289855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:41.522849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:44.976126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:48.291916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:51.664800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:54.984406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:58.480609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:01.913451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:05.153120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:08.455976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:11.882193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:15.735067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:38.539801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:41.782734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:45.245733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:48.571859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:51.938088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:55.254332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:58.743969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:02.190168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:05.403030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:08.722692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:12.152281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:16.005056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:38.813093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:42.056040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:45.522179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:48.848463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:52.221435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:55.544389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:59.020507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:02.473424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:05.669712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:09.002586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:12.438793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:16.285018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:39.079758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:42.312658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:45.799232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:49.125125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:52.498003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:55.824268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:59.310517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:02.746702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:05.929700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:09.282572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:12.705482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:16.561654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:39.356436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:42.579356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:46.082226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:49.408416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:52.764632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:56.100907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:44:59.587117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:03.023389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:06.203228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:09.562590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-15T01:45:12.985482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-15T01:45:24.672451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-15T01:45:24.852354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-15T01:45:25.022264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-15T01:45:25.172191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-15T01:45:25.272129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-15T01:45:17.048163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-15T01:45:18.097932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-15T01:45:19.511178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idnamepopularityduration_msexplicitartistsid_artistsrelease_datedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
035iwgR4jXetI318WEWsa1QCarve61269030['Uli']['45tIt06XoI0Iio4LBEVpls']1922-02-220.6450.44500-13.33810.45100.6740.7440000.15100.1270104.8513
1021ht4sdgPcrDgSk7JTbKYCapítulo 2.16 - Banquero Anarquista0982000['Fernando Pessoa']['14jtPCOoNZwquk5wd9DxrY']1922-06-010.6950.26300-22.13610.95700.7970.0000000.14800.6550102.0091
207A5yehtSnoedViJAZkNncVivo para Quererte - Remasterizado01816400['Ignacio Corsini']['5LiOoJbxVSAMkBS2fUm3X2']1922-03-210.4340.17701-21.18010.05120.9940.0218000.21200.4570130.4185
308FmqUhxtyLTn6pAh6bk45El Prisionero - Remasterizado01769070['Ignacio Corsini']['5LiOoJbxVSAMkBS2fUm3X2']1922-03-210.3210.09467-27.96110.05040.9950.9180000.10400.3970169.9803
408y9GfoqCWfOGsKdwojr5eLady of the Evening01630800['Dick Haymes']['3BiJGZsyX9sJchTqcSA7Su']19220.4020.15803-16.90000.03900.9890.1300000.31100.1960103.2204
50BRXJHRNGQ3W4v9frnSfhuAve Maria01789330['Dick Haymes']['3BiJGZsyX9sJchTqcSA7Su']19220.2270.26105-12.34310.03820.9940.2470000.09770.0539118.8914
60Dd9ImXtAtGwsmsAD69KZTLa Butte Rouge01344670['Francis Marty']['2nuMRGzeJ5jJEKlfS7rZ0W']19220.5100.35504-12.83310.12400.9650.0000000.15500.727085.7545
70IA0Hju8CAgYfV1hwhidBHLa Java01614270['Mistinguett']['4AxgXfD7ISvJSTObqm4aIE']19220.5630.18404-13.75710.05120.9930.0000160.32500.6540133.0883
80IgI1UCz84pYeVetnl1lGPOld Fashioned Girl03100730['Greg Fieler']['5nWlsH5RDgFuRAiDeOFVmf']19220.4880.47500-16.22200.03990.6200.0064500.10700.5440139.9524
90JV4iqw2lSKJaHBQZ0e5zKMartín Fierro - Remasterizado01811730['Ignacio Corsini']['5LiOoJbxVSAMkBS2fUm3X2']1922-03-290.5480.03916-23.22810.15300.9960.9330000.14800.612075.5953

Last rows

idnamepopularityduration_msexplicitartistsid_artistsrelease_datedanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signature
5866624Zp3rm12p5PiHToYJflmyyMeet Again572735870['KIMSEJEONG']['1lFLniFTaPjYCtQZvDXpqu']2020-12-200.4760.44003-8.50810.04880.6790.0000000.09260.2410135.8144
5866634ow9HehIdFii1cggylW2k0四季予你 - DJ版471563930['程響', '阿卓']['7nKA1c1Qn6nI0XA8yburf3', '7g8hOWXtGS16J30CMU1SR7']2020-12-290.6770.97000-3.38800.04460.1340.0023400.30200.9080140.0264
5866641Kzjk1EyngBcP4T8x3fyqv同行 (新加坡電視劇《愛...沒有距離》主題曲)432052380['Boon Hui Lu']['6PWJWwEm8BSBFAIAUWlwe4']2020-03-030.7430.67908-3.95210.03230.2690.0000000.13300.3950126.0704
5866650SjsIzJkZfDU7wlcdklEFRJohn Brown's Song661852500['Gregory Oberle']['4MxqhahGRT4BPz1PilXGeu']2020-03-200.5620.03311-25.55110.10300.9960.9610000.11100.386063.6963
5866661ZwZsVZUiyFwIHMNpI3ERtSkyscraper41060020['Emilie Chin']['4USdOnfLczwUglA3TrdHs2']2020-02-080.6260.53005-13.11700.02840.1130.8560000.10400.2150120.1134
5866675rgu12WBIHQtvej2MdHSH0云与海502582670['阿YueYue']['1QLBXKM5GCpyQQSVMNZqrZ']2020-09-260.5600.51800-7.47100.02920.7850.0000000.06480.2110131.8964
5866680NuWgxEp51CutD2pJoF4OMblind721532930['ROLE MODEL']['1dy5WNgIKQU6ezkpZs4y8z']2020-10-210.7650.66300-5.22310.06520.1410.0002970.09240.6860150.0914
58666927Y1N4Q4U3EfDU5Ubw8ws2What They'll Say About Us701876010['FINNEAS']['37M5pPGs6V1fchFJSgCguX']2020-09-020.5350.31407-12.82300.04080.8950.0001500.08740.0663145.0954
58667045XJsGpFTyzbzeWK8VzR8SA Day At A Time581420030['Gentle Bones', 'Clara Benin']['4jGPdu95icCKVF31CcFKbS', '5ebPSE9YI5aLeZ1Z2gkqjn']2021-03-050.6960.615010-6.21210.03450.2060.0000030.30500.438090.0294
5866715Ocn6dZ3BJFPWh4ylwFXtnMar de Emociones382143600['Afrosound']['0i4Qda0k4nf7jnNHmSNpYv']2015-07-010.6860.72306-7.06710.03630.1050.0000000.26400.9750112.2044